Detection of rice plant diseases based on deep transfer learning
Detection of rice plant diseases based on deep transfer learning
复制标题
基于深度迁移学习的水稻病害检测
DOI：
10.1002/jsfa.10365
复制
发表时间：
2020-03-14
影响因子：
4.1
通讯作者：
Li, Dele
中科院分区：
文献类型：
--
作者：
Chen, Junde;Zhang, Defu;Li, Dele
作为世界上近一半人口的主要食物，水稻几乎在世界各地种植，特别是在亚洲国家。然而，几个世纪以来，农民和种植专家一直面临着许多持续的农业挑战，例如水稻的各种疾病。严重的水稻病害可能导致粮食绝收，因此，一种快速、自动、低成本和准确的水稻病害检测方法是农业信息领域的迫切需求。结果在本文中，我们研究了用于解决该任务的深度学习方法，因为它在图像处理和分类问题中表现出出色的性能。结合两者的优点，选择在ImageNet和Inception模块上预训练的DenseNet用于网络中，这种方法相对于其他最先进的方法具有上级性能。在公开数据集上，平均预测准确率不低于94.07%。即使在考虑多种病害的情况下，对水稻病害图像的分类预测的平均准确率也达到98.63%。结论实验结果证明了该方法的有效性，可以有效地实现水稻病害的检测。(c)2020化学工业协会
BACKGROUND As the primary food for nearly half of the world's population, rice is cultivated almost all over the world, especially in Asian countries. However, the farmers and planting experts have been facing many persistent agricultural challenges for centuries, such as different diseases of rice. The severe rice diseases may lead to no harvest of grains; therefore, a fast, automatic, less expensive and accurate method to detect rice diseases is highly desired in the field of agricultural information.RESULTS In this article, we study the deep learning approach for solving the task since it has shown outstanding performance in image processing and classification problem. Combining the advantages of both, the DenseNet pre-trained on ImageNet and Inception module were selected to be used in the network, and this approach presents a superior performance with respect to other state-of-the-art methods. It achieves an average predicting accuracy of no less than 94.07% in the public dataset. Even when multiple diseases were considered, the average accuracy reaches 98.63% for the class prediction of rice disease images.CONCLUSIONS The experimental results prove the validity of the proposed approach, and it is accomplished efficiently for rice disease detection. (c) 2020 Society of Chemical Industry